Camouflage methods to counter artificial intelligence recognition

In the last years AI based algorithms have significantly increased in both popularity and in efficiency for numerous applications. As those artificial neuronal networks can also be used for military reconnaissance, is it necessary to think about methods to avoid or impede enemy detection or recognition by automated AI systems. However, the features that make an object salient to a human observer are not transferable to AI-based systems, since the features that the AI uses to classify things are mostly learning-data dependent and obscure. In this work, we aim to show ways to understand AI's decisions using LIME or Grad-CAM, and thus find ways to decrease classification performance in order to develop a camouflage against AI, or to decept it with adversarial attacks. Camouflage measures can then be evaluated using these methods for their effectiveness against AI, and by combining this with camouflage performance evaluation against human observers using existing methods we try to find the best possible tradeoff for combined camouflage against both threats.